All papers

This paper introduces a framework that enables language models to optimize physical structural designs through simulated fracture experience and hypothesis revision.

This paper presents a coupled evolution framework that co-adapts an executable tool library and a model's policy for scientific equation discovery.

Presents an exact machine unlearning method for federated socially fair k-means clustering using separate group-level local summaries.

This paper proposes a training framework that triggers supervised fine-tuning specifically on failure states during reinforcement learning to prevent agent forgetting.

This paper introduces a training-free safety filter that evaluates scene context to constrain generated humanoid motion trajectories.

Introduces an adaptive-group process reward framework that partitions GUI trajectories into subgoals to provide intermediate credit assignment for reinforcement learning.

This paper presents a feasibility-aware safety filter that amortizes minimal-intervention constraints using a feedforward neural network in reinforcement learning.

The paper proposes a multi-agent framework to coordinate analytical tools and evaluate multi-factor supply chain risks across diverse locations.

This paper develops a unified Bellman operator to optimize task performance and enforce constraints within safety-critical reinforcement learning.

The paper introduces an agentic system that reconstructs multi-layer 3D garment sewing patterns from a single image using simulation feedback.

The paper proposes a credal machine learning framework that models epistemic uncertainty with probability distribution sets for risk-averse decision-making.

The paper establishes and validates a set of meta-requirements to guide the design of frameworks for auditing AI system robustness.

Proposes a multi-route retrosynthesis planner that evaluates synthesizability and cost across chemical reaction options.

The paper argues that uncertainty estimation in smart assistants is necessary to prevent unsafe actions when user preferences remain ambiguous.

The paper argues that full robotic value alignment is impossible and examines how to manage acceptable misalignment in autonomous systems.

The paper presents GENDIS, a multilingual dataset of YouTube comments from India, Kenya, and Nigeria annotated for gender-related discourse and stance.

Uses agentic program search to iteratively write and refine design algorithms for nucleic acid sequences that transfer across independent models.

This paper evaluates whether attention-graph curvature features can help verifiers detect errors in mathematical reasoning steps.

This paper introduces GUIDE, a multi-agent copilot system that assists clinicians in drafting and updating evidence-based medical practice guidelines.

The paper presents a byte-local text watermarking method that scores outputs without invoking tokenizers during detection.

The paper presents a hierarchical agent combining vision-language models for task planning with deterministic control programs for micromanipulation.

The paper introduces a conflict-clustering solver with lightweight learning to coordinate distributed agents solving constraint satisfaction problems.

Studies uncertainty estimation for abstract meaning representation parsers using top-k beam candidates and prediction rejection ratios.

The paper presents an ontology-guided retrieval framework combining population and personalized memory for wearable sensor human activity recognition.

Introduces an immutable, provenance-aware state representation framework with deterministic replay and audit logging for robotic systems.

Proposes a hierarchical evolution framework that allows individual agent software harnesses in multi-agent systems to adapt using feedback.

Proposes a conformal prediction framework that learns stochastic groupings to improve subgroup coverage reliability without inflating overall prediction sets.

Uses multi-objective evolutionary algorithms to optimize agent execution harnesses across competing metrics like accuracy and operating cost.

Tests whether priming annotators with scenario vignettes and micro-narratives improves the operationalization of lived experience in subjective annotation.

The paper demonstrates an on-chip power side-channel attack reconstructing color ImageNet inputs from machine learning accelerators on shared multi-tenant FPGAs.

This paper trains language models using reinforcement learning to construct executable financial trading formulas grounded in research literature.

The paper introduces a black-box method to extract training sequences from genomic language models using output sampling and sequence alignment.

This paper presents an experience-based learning framework that uses trajectory quality signals rather than final task outcomes to guide agent improvement.

This paper presents an interface framework applying visual transformations and adversarial attacks to create assignment text resistant to machine extraction.

This paper presents an on-device communication agent that uses small language models to generate low-latency mobile network configuration rules.

This paper proposes an aggregator-agnostic watermarking scheme to embed persistent watermarks into models during federated learning despite robust aggregators.

This position paper outlines a conceptual operating model and set of principles for integrating persistent AI agents into product management.

This paper introduces a server-side defense against active label inference attacks in vertical federated learning using embedding asymmetry signals.

The paper introduces a reinforcement learning approach for multi-turn agents that identifies recurring decision patterns to improve intermediate step credit assignment.

This paper proposes an external memory-based framework to unlearn specific identities from trained models when training data is absent.

The paper proposes a structured chain-of-thought grounding method to track visual evidence across video frames for goal-oriented embodied navigation.

This paper presents a multi-agent framework that translates natural language into verified, executable data analytical programs using schema-grounded refinement.

The paper proposes certified adversarial detection using Lipschitz-margin analysis to detect perturbations rather than forcing invariant predictions.

This paper proposes a counterfactual certification framework to bound performance loss during sequential machine unlearning requests.

This paper proposes a critic-free reinforcement learning algorithm using prefix trees to assign step-level credit for interactive multi-step tasks.

The authors analyze robustly reliable learning for regression under data corruptions and propose an algorithm to compute optimal prediction intervals for Lipschitz predictors.

This paper presents a framework that uses history tracking and problem-specific tools to help language agents solve complex combinatorial optimization tasks.

The paper proposes a fine-tuning method that trains language model agents using textual expert critiques alongside corrected actions.

This paper presents a psychometric framework to test whether automated sentiment classification remains equivalent across different social media platforms.

Presents an automated framework using language models to manage storytelling, rules, and combat in tabletop role-playing games.

This paper measures whether ecological alignment in large language models survives under pressure using a calibrated pretraining ladder.

The paper introduces a hierarchical memory agent framework for multi-center medication recommendation that accounts for hospital differences and drug safety constraints.

This paper introduces a data-flow graph representation and mutation testing technique to uncover bugs in deep learning frameworks.

The paper compares three textual interface designs for optimizing prompts in multimodal audio-visual question answering systems.

This paper proposes a hierarchy of sparse second-order cone programming relaxations for certified neural network robustness.

The paper proposes a vision-language-action model that dynamically crops high-resolution regions to manipulate objects without using a wrist camera.

The paper presents a framework using random noise and Sigmoid function analysis to detect directional adversarial attacks on medical imaging models.

The paper develops methods to evaluate and recalibrate binary predictors using noisy, multi-annotator labels without requiring clean ground truth.

The paper proposes a progressive validation framework for zero-shot tool selection that evaluates functional compatibility and contextual relevance.

The paper proposes a sequential conformal prediction algorithm that groups calibration scores by state similarity rather than pure temporal recency.